III: Small: High-Throughput Annotation of Cellular Functions of Intrinsic Disorder in Proteins

分子生物学中的一个基本问题是破译由高通量基因组测序快速产生的数百万个未表征的蛋白质序列的功能。几十年来，人们一直使用从序列到结构再到功能的范式来确定蛋白质的功能。然而，最近的研究通过增加新的参与者，具有内在障碍的蛋白质(ID)，拓宽了这一范式。它们是高度丰富的，不能用结构驱动的方法解决。虽然有许多广泛使用的计算方法可以准确地预测蛋白质序列中的ID，但缺乏预测ID的许多功能的方法。该项目将开发一系列新颖、准确和高通量的计算方法，预测蛋白质序列中ID的所有主要功能。它将以前所未有的规模产生数以千计的物种的假定功能注释，解决高速获取原始序列数据的问题，并有助于提高科学发现的速度。鉴于ID在人类疾病中的高患病率以及以ID为药物靶点的蛋白质的吸引力，这些结果将促进我们对基本生物学过程和人类健康的理解。该项目还将通过短期研讨会以及本科生和研究生水平的讲座和辅导，帮助培训STEM学生和研究人员，重点展示蛋白质生物信息学新兴领域的教育和研究的相关性和价值。该项目将概念化、设计、严格测试和部署固有疾病的所有主要功能的预测因子，包括蛋白质-RNA、蛋白质-DNA、蛋白质-蛋白质、蛋白质-配体和蛋白质-脂相互作用、柔性连接物和间隔区、翻译后修饰的区域以及兼职区域。当常用的基于序列比对的方法失败时，这些方法将在缺乏序列相似性的情况下提供快速和准确的预测。该设计将包括一种新颖的、特定于函数的混合特征提取、经验特征选择以及使用现代机器学习算法生成的预测模型的优化。这些预测模型的输入将从一套全面的经验选择和序列派生的蛋白质结构和生物物理特征中进行量化和汇总。由此产生的方法将进行基准测试，并通过基于网络的服务器端门户网站免费提供给广大研究社区。结果也将存入相关的公共数据库。

One of fundamental problems in molecular biology is to decipher functions of millions of uncharacterized protein sequences that are rapidly generated by high-throughput genome sequencing. The sequence-to-structure-to-function paradigm was used for decades to determine functions of proteins. However, recent research has broadened this paradigm by adding new players, proteins with intrinsic disorder (ID). They are highly abundant and cannot be solved with the structure-driven approach. While there are many widely used computational methods that accurately predict ID in protein sequences, methods for the prediction of the many functions of ID are lacking. This project will develop a family of novel, accurate, and high-throughput computational methods that predict all major functions of ID in protein sequences. It will produce putative functional annotations on an unprecedented scale of thousands of species, addressing the problem of high rate acquisition of raw sequence data and contributing to the increase of the rate of scientific discovery. These results will advance our understanding of fundamental biological processes and human health given the high prevalence of ID in human diseases and attractiveness of proteins with ID as drug targets. This project will also contribute to training of STEM students and researchers via short workshops and undergraduate and graduate level lectures and mentoring, focusing on demonstrating relevance and value of education and research in the emerging areas of protein bioinformatics.This project will conceptualize, design, rigorously test and deploy predictors of all major functions of intrinsic disorder including protein-RNA, protein-DNA, protein-protein, protein-ligand and protein-lipid interaction, flexible linkers and spacers, regions that host post-translational modifications, and moonlighting regions. These methods will provide fast and accurate predictions in the absence of sequence similarity when the commonly used sequence alignment-based approaches fail. The design will include a novel, hybrid function-specific feature extraction, empirical feature selection, and optimization of predictive models generated with modern machine learning algorithms. The inputs to these predictive models will be quantified and aggregated from a comprehensive set of empirically selected and sequence-derived structural and biophysical characteristics of proteins. The resulting methods will be benchmarked and made freely available to the broad research community via a web-based, server-side portal. The results will be also deposited into relevant public databases.